Notice the date -- just saw it myself.
http://developer.amd.com/community/blog/2013/08/13/amd-releases-appml-source-code-creates-clmath-library/
AMD releases APPML source code, creates clMath library
Posted by kknox on August 13, 2013 in AMD, AMD APP, AMD Libraries, APU,
Developer with 3 comments
For the past three years, the AMD library team has been heads down, working
on an important project – the Accelerated Parallel Processing Math Library.
APPML contains an OpenCL implementation of BLAS and FFT routines. The library
enables developers to accelerate common scientific and engineering
computations on APUs and discrete graphics accelerators. Under the right
conditions, we can accelerate them A LOT. After 6 caffeine-powered releases,
I’m proud of the work we accomplished. Along the way, we received numerous
requests to participate in the library. We heard you, and we believe that
the time is right to take the project to the next level.
It brings me great pleasure to announce that in close collaboration with
AccelerEyes, we are opening the source to the APPML project and making it
available on GitHub under the name clMath (read more about AccelerEyes
comments here: http://www.accelereyes.com/clmath). AccelerEyes engineers are
dedicating significant resources for continued development of the clMath
library, and we intend for this project to be a focal point for
collaboration. With AccelerEyes, we welcome adoption and encourage
contributions to the source. We look forward to your voice in this
conversation.
The clMath source will be licensed under the Apache License, Version 2.0
http://www.apache.org/licenses/LICENSE-2.0.html. The source also includes
our test and performance infrastructure. Our hope is that the community will
embrace and improve the existing code, and keep the projects going for years
to come!
You will find the new clMath projects at the following URL’s:
https://github.com/clMathLibraries/clBLAShttps://github.com/clMathLibraries/clFFT
and we have also created two mailing lists for the clMath projects to help
facilitate communication between users and developers of the libraries
respectively:
clmath at googlegroups.comclmath-developers at googlegroups.com
Both projects compile for Linux and Windows and the associated project wiki
pages contain build instructions and other related project documentation;
interested developers should make sure to scan through the wiki.
Kent Knox has spent 15 years as a developer at AMD and has served as the
technical lead for the APPML and Bolt projects. His postings are his own
opinions and may not represent AMD’s positions, strategies or opinions. Links
to third party sites, and references to third party trademarks, are provided
for convenience and illustrative purposes only. Unless explicitly stated, AMD
is not responsible for the contents of such links, and no third party
endorsement of AMD or any of its products is implied.
There are 3 comments.
Sebastian — August 21, 2013 @ 11:58 am
Kent, this is awesome. An open-source FFT/BLAS library will open the
possibility of merging FFT/BLAS kernels with other kernels. We should now
also be able to tweak functions for specific use cases if we so desire.
I was wondering if there is a chance to test-drive an AMD GPU using OpenCL.
So far we’re an Nvidia shop only, but if I can show that AMD GPUs are
equivalent or surpass Nvidia in terms of performance (FFT+BLAS), we might
just order a few AMD GPUs. You can contact me under sschaet – at – gwdg dot
de.
Cheers,
Sebastian
Reply »
kknox — August 21, 2013 @ 10:34 pm
Thank you for leaving your feedback Sebastian; I have forwarded your comment
on to our business relationship guys. If you have technical questions or
comments, leave feedback on our GitHub repositories or our APPML forums
(http://devgurus.amd.com/community/appml) and I’ll see the comments there.
Kent
Reply »
Alan — August 29, 2013 @ 10:29 pm
Thank you so much for releasing this (especially for releasing the source!).
At a previous job, we found that a 7970 blew away the Tesla and GTX 680 cards
we’d previously been using for some DX11 computer vision applications (20-40%
performance improvement depending on the particular algorithm).
In my current job, we’ve been using CUDA mainly because of the lack of an
official BLAS package for OpenCL. Now that this is out, I think it’s feasible
for me to do some benchmarking to see if switching to AMD cards will be a win
for us. Please continue turning out useful stuff like this!
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